<![CDATA[Parkinson’s disease is the second‐most‐common neurodegenerative disorder and can reduce patients’ quality of life. The disease is caused by abnormalities in dopaminergic neurons, such as reactive oxygen species (ROS) imbalance leading to programmed cell death, protein misfolding, and vesicle trafficking. Protein‐protein interaction (PPI) analysis has been demonstrated to understand better candidate proteins that might contribute to multifactorial neurodegenerative diseases, particularly in Parkinson’s disease. PPI analysis can be obtained from experiments and computational predictions. However, experiment data is often limited in interactome coverage. Therefore, additional computational prediction methods are required to provide more comprehensive PPI information. PPI can be represented as protein‐protein networks and analyzed based on centrality measures. The previous study has shown that top‐k skyline query, a method using dominance rule‐based centrality measures, reveals important protein candidates in Parkinson’s diseases. This study applied the top‐k skyline query to PPIs containing experiment and prediction data to find important proteins in Parkinson’s disease. The result shows that alpha‐synuclein (SNCA) is the most important protein and is expected to be a potential biomarker candidate for Parkinson’s disease.]]>
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